People analytics is a data-driven approach to managing people at work. For the first time in history, business leaders can make decisions about their people based on deep analysis of data rather than the traditional methods of personal relationships, decision making based on experience, and risk avoidance. In this brand new course, three of Wharton’s top professors, all pioneers in the field of people analytics, will explore the state-of-the-art techniques used to recruit and retain great people, and demonstrate how these techniques are used at cutting-edge companies. They’ll explain how data and sophisticated analysis is brought to bear on people-related issues, such as recruiting, performance evaluation, leadership, hiring and promotion, job design, compensation, and collaboration. This course is an introduction to the theory of people analytics, and is not intended to prepare learners to perform complex talent management data analysis. By the end of this course, you’ll understand how and when hard data is used to make soft-skill decisions about hiring and talent development, so that you can position yourself as a strategic partner in your company’s talent management decisions. This course is intended to introduced you to Organizations flourish when the people who work in them flourish. Analytics can help make both happen. This course in People Analytics is designed to help you flourish in your career, too.

ST

Real helpful course especially for individuals who are in HR field or interested in becoming a better leaders, team players. Professors incorporated data analytics into human talent management!

BC

Dec 15, 2017

Filled StarFilled StarFilled StarFilled StarFilled Star

Excellent course! I learned more about HR and staffing strategy from this single course than the Human Resource Management specialization from the other university program offered on Coursera.

From the lesson

Introduction to People Analytics, and Performance Evaluation

In this module, you'll meet Professors Massey, Bidwell, and Haas, cover the structore and scope of the course, and dive into the first topic: Performance Evaluation. Performance evaluation plays an influential role in our work lives, whether it is used to reward or punish and/or to gather feedback. Yet its fundamental challenge is that the measures we used to evaluate performance are imperfect: we can't infer how hard or smart an employee is working based solely on outcomes. In this module, you’ll learn the four key issues in measuring performance: regression to the mean, sample size, signal independence, and process vs. outcome, and see them at work in current companies, including an extended example from the NFL. By the end of this module, you’ll understand how to separate skill from luck and learn to read noisy performance measures, so that you can go into your next performance evaluation sensitive to the role of chance, knowing your environment, and aware of the four most common biases, so that you can make more informed data-driven decisions about your company's most valuable asset: its employees.

Taught By

Cade Massey

Practice Professor

Martine Haas

Associate Professor of Management

Matthew Bidwell

Associate Professor of Management

Transcript

So the basic goal of this course is to introduce people to the major areas of people analytics, and help them to understand what it is as a field. We can't promise that we're gonna make you experts over the course of these few sessions. What instead we want to do, is help you understand what's going on here, and if you want to move forward, Get interested in this field, what skills do you need to know. Ultimately to be fully competent here, there's a bunch of statistics you should probably catch up on. Maybe even a certain amount of data management expertise that you would know. We at least hope to provide some sort of introduction. And so really we're gonna aim to do a few things. First of all, we want to provide you some understanding of what are the main areas of activity in people analytics today. What are the main decisions that are increasingly being informed by data? In order to help get a sense of that, we also want to provide some sort of conceptual underpinning. Both of kind of the general applications of data to these decisions But also of the various different decisions that we made. What do we know about how people behave in organizations that we need to include when we do these sorts of analyses. On top of that, we wanna give you some sense of how you could actually go about doing this. What kind of data are there within the organization, which you could go out and gather, to inform some of these decisions. And then how should you think about analyzing it? So, in part that to understanding some of the pitfalls, okay, what are the areas where people go wrong. And in addition, it's understanding what the basics of getting it right and kind of a brief preview of what are some of the most sophisticated areas where people are introducing analytics and was kind of some of the promise of that. So those are our basic goals.

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